Paper A v3.20.0: partner Jimmy 2026-04-27 review + DOCX rendering overhaul

Substantive content (addresses partner Jimmy's 2026-04-27 review of v3.19.1):

Must-fix items (6/6):
- §III-F SSIM/pixel rejection rewritten from first principles (design-level
  argument from luminance/contrast/structure local-window product, not the
  prior empirical 0.70 result)
- Table VI restructured by population × method; added missing Firm A
  logit-Gaussian-2 0.999 row; KDE marked undefined (unimodal), BD/McCrary
  marked bin-unstable (Appendix A)
- Tables IX / XI / §IV-F.3 dHash 5/8/15 inconsistency resolved: ≤8 demoted
  from "operational dual" to "calibration-fold-adjacent reference"; the
  actual classifier rule cos>0.95 AND dH≤15 = 92.46% added throughout
- New Fig. 4 (yearly per-firm best-match cosine, 5 lines, 2013-2023, Firm A
  on top); script 30_yearly_big4_comparison.py
- Tables XIV / XV extended with top-20% (94.8%) and top-30% (81.3%) brackets
- §III-K reframed P7.5 from "round-number lower-tail boundary" to operating
  point; new Table XII-B (cosine-FAR-capture tradeoff at 5 thresholds:
  0.9407 / 0.945 / 0.95 / 0.977 / 0.985)

Nice-to-have items (3/3):
- Table XII expanded to 6-cut classifier sensitivity grid (0.940-0.985)
- Defensive parentheticals (84,386 vs 85,042; 30,226 vs 30,222) moved to
  table notes; cut "invite reviewer skepticism" and "non-load-bearing"

Codex 3-pass verification cleanup:
- Stale 0.973/0.977/0.979 references unified on canonical 0.977 (Firm A
  Beta-2 forced-fit crossing from beta_mixture_results.json)
- dHash≤8 wording corrected to P95-adjacent (P95 = 9, ≤8 is the integer
  immediately below) instead of misleading "rounded down"
- Table XII-B prose corrected: per-segment qualification of "non-Firm-A
  capture falls faster" (true on 0.95→0.977 segment but contracts on
  0.977→0.985 segment); arithmetic now from exact counts

Within-year analyses removed:
- Within-year ranking robustness check (Class A) was added in nice-to-have
  pass but contradicts v3.14 A2-removal stance; removed from §IV-G.2 + the
  Appendix B provenance row
- Within-CPA future-work disclosures (Class B) removed from Discussion
  limitation #5 and Conclusion future-work paragraph; subsequent limitations
  renumbered Sixth → Fifth, Seventh → Sixth

DOCX rendering pipeline overhaul (paper/export_v3.py):

Critical fix - every v3 DOCX since v3.0 was shipping WITHOUT TABLES:
strip_comments() was wholesale-deleting HTML comments, but every numerical
table is wrapped in <!-- TABLE X: ... -->, so the table body was deleted
alongside the wrapper. Now unwraps TABLE comments (emit synthetic
__TABLE_CAPTION__: marker + table body) while still stripping non-TABLE
editorial comments. Result: 19 tables now render in the DOCX.

Other rendering fixes:
- LaTeX → Unicode conversion (50+ token replacements: Greek alphabet, ≤≥,
  ×·≈, →↔⇒, etc.); \frac/\sqrt linearisation; TeX brace tricks ({=}, {,})
- Math-context-scoped sub/superscript via PUA sentinels (/):
  no more underscore-eating in identifiers like signature_analysis
- Display equations rendered via matplotlib mathtext to PNG (3 equations:
  cosine sim, mixture crossing, BD/McCrary Z statistic), embedded as
  numbered equation blocks (1), (2), (3); content-addressed cache at
  paper/equations/ (gitignored, regenerable)
- Manual numbered/bulleted list rendering with hanging indent (replaces
  python-docx style="List Number" which silently drops the number prefix
  when no numbering definition is bound)
- Markdown blockquote (> ...) defensively stripped
- Pandoc footnote ([^name]) markers no longer leak (inlined at source)
- Heading text cleaned of LaTeX residue + PUA sentinels
- File paths in body text (signature_analysis/X.py, reports/Y.json)
  trimmed to "(reproduction artifact in Appendix B)" pointers

New leak linter: paper/lint_paper_v3.py - two-pass markdown source +
rendered DOCX leak detector; auto-runs at end of export_v3.py.

Script changes:
- 21_expanded_validation.py: added 0.9407, 0.977, 0.985 to canonical FAR
  threshold list so Table XII-B is reproducible from persisted JSON
- 30_yearly_big4_comparison.py: NEW; generates Fig. 4 + per-firm yearly
  data (writes to reports/figures/ and reports/firm_yearly_comparison/)
- 31_within_year_ranking_robustness.py: NEW; supports the within-year
  robustness check (no longer cited in paper but kept as repo-internal
  due-diligence artifact)

Partner handoff DOCX shipped to
~/Downloads/Paper_A_IEEE_Access_Draft_v3.20.0_20260505.docx (536 KB:
19 tables + 4 figures + 3 equation images).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

paper/paper_a_abstract_v3.md

@@ -2,6 +2,6 @@
 
 <!-- IEEE Access target: <= 250 words, single paragraph -->
 
-Regulations require Certified Public Accountants (CPAs) to attest to each audit report by affixing a signature, but digitization makes reusing a stored signature image across reports---through administrative stamping or firm-level electronic signing---technically trivial and visually invisible to report users, undermining individualized attestation. We build an end-to-end pipeline that detects such *non-hand-signed* signatures at scale: a Vision-Language Model identifies signature pages, a YOLOv11 detector localizes signature regions, ResNet-50 supplies deep features, and a dual-descriptor verification layer combines deep-feature cosine similarity with perceptual hashing (difference hash, dHash) to separate *style consistency* (high cosine, divergent dHash) from *image reproduction* (high cosine, low dHash). The operational classifier outputs a five-way verdict per signature with a worst-case document-level aggregation; the cosine cut is anchored on a transparent whole-sample Firm A P7.5 percentile (cos $> 0.95$), and the dHash cuts on the same reference. Applied to 90,282 audit reports filed in Taiwan over 2013-2023 (182,328 signatures from 758 CPAs), the operational dual rule cos $> 0.95$ AND $\text{dHash}_\text{indep} \leq 8$ captures 89.95\% of Firm A and yields FAR $\leq$ 0.001 against a $\sim$50,000-pair inter-CPA negative anchor; intra-report agreement is 89.9\% at Firm A versus 62-67\% at the other Big-4 firms (a 23-28 percentage-point cross-firm gap). Validation uses three annotation-free anchors (310 byte-identical positives, $\sim$50,000 inter-CPA negatives, and a 70/30 held-out Firm A fold) reported with Wilson 95\% intervals. Three statistical diagnostics applied to the per-signature similarity distribution (Hartigan dip test, EM-fitted Beta mixture with logit-Gaussian robustness check, Burgstahler-Dichev / McCrary density-smoothness procedure) jointly characterise the distribution as a continuous quality spectrum, which motivates the percentile-based anchor and is itself a substantive finding for similarity-threshold selection in document forensics.
+Regulations require Certified Public Accountants (CPAs) to attest to each audit report by affixing a signature, but digitization makes reusing a stored signature image across reports---through administrative stamping or firm-level electronic signing---technically trivial and visually invisible to report users, undermining individualized attestation. We build an end-to-end pipeline that detects such *non-hand-signed* signatures at scale: a Vision-Language Model identifies signature pages, a YOLOv11 detector localizes signature regions, ResNet-50 supplies deep features, and a dual-descriptor verification layer combines deep-feature cosine similarity with perceptual hashing (difference hash, dHash) to separate *style consistency* (high cosine, divergent dHash) from *image reproduction* (high cosine, low dHash). The operational classifier outputs a five-way verdict per signature with a worst-case document-level aggregation; the cosine cut is anchored on a transparent whole-sample Firm A P7.5 percentile (cos $> 0.95$), and the dHash cuts on the same reference. Applied to 90,282 audit reports filed in Taiwan over 2013-2023 (182,328 signatures from 758 CPAs), the operational dual rule cos $> 0.95$ AND $\text{dHash}_\text{indep} \leq 15$ captures 92.46\% of Firm A and yields FAR = 0.0005 against a $\sim$50,000-pair inter-CPA negative anchor; intra-report agreement is 89.9\% at Firm A versus 62-67\% at the other Big-4 firms (a 23-28 percentage-point cross-firm gap). Validation uses three annotation-free anchors (310 byte-identical positives, $\sim$50,000 inter-CPA negatives, and a 70/30 held-out Firm A fold) reported with Wilson 95\% intervals. Three statistical diagnostics applied to the per-signature similarity distribution (Hartigan dip test, EM-fitted Beta mixture with logit-Gaussian robustness check, Burgstahler-Dichev / McCrary density-smoothness procedure) jointly characterise the distribution as a continuous quality spectrum, which motivates the percentile-based anchor and is itself a substantive finding for similarity-threshold selection in document forensics.
 
 <!-- Target word count: 240 -->